orthog_post_fitting

Orthogonalize a Semi-Structured Model Post-hoc

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

orthog_post_fitting function

<dl><dt>mod</dt>
<dd>deepregression model</dd>
<dt>name_penult</dt>
<dd>character name of the penultimate layer 
of the deep part part</dd>
<dt>param_nr</dt>
<dd>integer; number of the parameter to be returned</dd></dl>

Arguments

Orthogonalize a Semi-Structured Model Post-hoc — orthog_post_fitting

<dl>

<dt>mod</dt>
<dd>deepregression model</dd>


<dt>name_penult</dt>
<dd>character name of the penultimate layer 
of the deep part part</dd>


<dt>param_nr</dt>
<dd>integer; number of the parameter to be returned</dd>

</dl>

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orthog_post_fitting: Orthogonalize a Semi-Structured Model Post-hoc

Description

Usage

Value

Arguments